Multi-objective optimization for a small biomass cooling and power cogeneration system using binary mixtures

This paper aims to find the optimal design for a small-scale cogeneration system using the Organic Rankine cycle (ORC) integrated with an ammonia-water absorption refrigeration cycle. The thermodynamic model included two ORC configurations (with and without an internal heat exchanger) and used eight Siloxanes and their possible mixtures as working fluids. Additionally, the effects of six design parameters were investigated, these being evaporation pressure, superheating, pinch point temperature, condensation pressure, internal heat exchanger effectiveness, and mass fraction in binary mixtures. A multi-objective genetic optimization algorithm (NSGA-II) was employed in order to evaluate the Pareto optimal solutions, maximizing electrical and cooling power and minimizing the overall global conductance, variables that have the greatest influence on the technical and economic performance of the cycle. Pareto optimal solutions present trade-offs between three objective functions, where optimum solutions are found within power generation ranges with overall energy efficiency between 18 and 30%. MM, MM/MD4M, MDM/MD4M and MDM/D-5, stand out as the best options for working fluids, with net power output between 700 and 1170 kW, cooling capacity between 80 and 1050 kW, and the product of overall heat transfer coefficient and overall global conductance in the range of 120-620 kW/K.

Automated summary

This study aims to optimize the design of a small-scale cogeneration system using ORC and absorption refrigeration cycle, evaluating the best solutions through a multi-objective genetic optimization algorithm. The results show that factors such as specific working fluids, net power output, and overall energy efficiency have significant impacts on the technical and economic performance of the cycle.